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January 1, 2026Open Access

A Survey on Machine Learning Techniques for Diabetic Type Classification

Dr.P.Suresh Babu Ms.M.Premavathi

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Key Points

The research explores machine learning methods to classify diabetes types for better patient management.
Data mining of medical datasets like Pima Indians Diabetes.
Pre-processing steps including imputation, outlier removal, normalization, and encoding.
Modeling with machine learning algorithms like Random Forest and SVM, and deep learning with DNN.
Pipeline optimization for increased accuracy in classification.
Achieved up to 97% accuracy in diabetes type classification.
Outperformed traditional methods in speed and precision.
Enabled scalable diagnosis across diverse datasets.

Implication

Survey reviews machine learning techniques for diabetic type classification, suggesting improved diagnostic efficiency.

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Cite This Study

Babu et al. (2026) studied this question.

synapsesocial.com/papers/69994cd2873532290d0219d2 https://doi.org/10.5281/zenodo.18696413

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